mindspore.ops.IndexAdd

class mindspore.ops.IndexAdd(*args, **kwargs)[source]

Adds tensor y to specified axis and indices of tensor x. The axis should be in the range from 0 to len(x.dim) - 1, and indices should be in the range from 0 to the size of x at the axis dimension.

Parameters: axis (int) – The dimension along which to index.

Inputs:

x (Parameter) - The input tensor to add to, with data type float64, float32, float16, int32, int16, int8, uint8.
indices (Tensor) - The index of x on the axis th dimension to add to, with data type int32. The indices must be 1D with the same size as the size of the axis th dimension of y. The values of indices should be in the range of 0 to the size of the axis th dimension of x.
y (Tensor) - The input tensor with the value to add. Must have same data type as x. The shape must be the same as x except the axis th dimension.

Outputs:

Tensor, has the same shape and dtype as x.

Raises

TypeError – If dtype of x is not one of: float64, float32, float16, int32, int16, int8, uint8.
TypeError – If neither indices nor y is a Tensor.
TypeError – If shape of y is not same as the x.
ValueError – If axis is out of x rank’s range.
ValueError – If x rank is not the same as y rank.
ValueError – If size of indices is not equal to dimension of y[axis].
ValueError – If y’s shape is not the same as x except the axis th dimension.

Supported Platforms:: GPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.index_add = ops.IndexAdd(axis=1)
...         self.x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32))
...         self.indices = Tensor(np.array([0, 2]), mindspore.int32)
...
...     def construct(self, y):
...         return self.index_add(self.x, self.indices, y)
...
>>> y = Tensor(np.array([[0.5, 1.0], [1.0, 1.5], [2.0, 2.5]]), mindspore.float32)
>>> net = Net()
>>> output = net(y)
>>> print(output)
[[ 1.5  2.   4. ]
 [ 5.   5.   7.5]
 [ 9.   8.  11.5]]